LIDAR is an active sensory system that uses light, laser light, to measure distances. When mounted in an airborne platform (fixed wing or rotary wing), this device can rapidly measure distances between the sensor on the airborne platform and points on the ground (or a building, tree, etc.) and is a proven approach to creating fast and accurate terrain models for applications in many types of industries. The technology is based on a scanning laser combined with both GPS and inertial technology to create a three dimensional set of points (point cloud). Airborne LIDAR mapping is an accurate and rapid method for three-dimensional surveying of the Earth’s surface and has become a widely used technique for generating high-resolution topographic data such as DEMs, DTMs and digital surface models (DSMs), with vertical accuracies of 15 to 100 centimetres (Hill and others, 2000). Topographic information is critical for a wide variety of purposes, including engineering projects (e.g., transportation, mining reclamations, urban planning), hydrology and floodplain management, corridor mapping (e.g., for roads, telecommunications), landside analysis, geological studies, and natural resource assessments. Elevation measurements from forest canopies, buildings, or other structures, along with ground information, are all available in raw LIDAR data. Post-processing techniques identify and remove non-ground features to produce accurate DEMs or bare-earth DTMs. High accuracies result because of the horizontal posting or point density of the LIDAR data, which can range from 1.5 to 9 meters.

LiDAR systems are an essential tool for improving the accuracy of both surface and sub-surface measurements and are also used to create detailed 3D models for ground exploration projects. 3D laser scanners capture highly accurate and detailed surface measurements by transmitting optical pulses that are reflected from the ground or surface feature. Using the time taken for each individual pulse to be returned and the known value of the speed of light, the system can accurately calculates the distance of the surface or feature from the scanning unit. In the oil and gas industry LiDAR is used to pre-select suitable locations in the office, which is quicker, safer and more cost-effective than sending survey crews to the field. Some uses of LiDAR data include:

Seismic programs and exploration

Location of well sites, facilities, and pipelines based on slope data

Selection of well locations and pipeline routes

Location and classification of buildings and other objects within special protection zones

Identification of land cover and timber removal calculations to minimize tree cutting

LIDAR data, when combined with digital orthophotos, can be used to create highly detailed Digital Surface Models (DSMs) and eventually Digital City Models. Using special software it is also possible to create estimated surface models of buildings from the original LiDAR data. This technology allows large area models to be created in a very short space of time. LIDAR is useful not only because it can provide accurate positions over large areas but also because it is fast: LIDAR can collect tens to hundreds of thousands of positions in a second. Collecting urban data at this level of detail manually would take years the buildings would likely fall down before the task was done. LIDAR is thus a viable solution to the massive task of mapping our urban infrastructure to support maintenance, modelling, and visioning exercises.

LIDAR provides detailed and highly accurate 3D data rapidly and efficiently, the ability of LIDAR to penetrate to the ground through gaps in tree canopies makes it especially attractive for generating bare earth terrain models in forested areas. In addition LIDAR can tell us much about the vegetation itself. Because LIDAR technology can capture elevation information from the forest canopy as well as the ground beneath and can be used to assess the complex three-dimensional patterns of canopy and forest-stand structure such as tree density, stand height, basal area, leaf area index, and forest biomass and volume (Kini and Popescu 2004). Knowledge about a community's vegetation composition, structure, and patterns is important for a variety of natural-resource planning and monitoring activities, including assessing fuel loads and fire risk, wildlife habitat, and impact from recreational activities as well as monitoring general forest trends and conditions. Forestry applications require a precise inventory of individual trees and groups, or "stands" of trees in order to address forest management and planning, study forest ecology and habitats, quantify forest fire fuel, and estimate carbon absorption.

LIDAR has many advantages as it creates highly accurate data sets in a faster timeframe than traditional approaches, effectively lowering potential costs,Compared to traditional survey methods which can be slow and costly, Airborne LIDAR Surveys can acquire millions of points within each square kilometre creating a robust data set for volume calculations, geomorphology, structural geology, design of transmission line or road corridors, slope analysis and run off surface modelling for feasibility and environmental impact studies.

LIDAR can produce topographic maps of amazing detail and accuracy, even where the ground is obscured by forest canopy. Detailed LIDAR topography can identify possible landing locations, difficult stream crossings, unstable soils, difficult side-slopes, and useful benches. This detail can reduce field time, guide road designs towards better options, and improve confidence in our cost estimates. High-resolution digital elevation maps generated by both airborne and stationary LIDAR systems have led to significant advances in geomorphology, geophysics and tectonics also using LIDAR with GPS has evolved and has become an important tool for detecting faults and for measuring uplift. The output of the two technologies can produce extremely accurate elevation models for terrain models that can even measure ground elevation through trees. This technique was used most famously used to measure the uplift at Mt. St. Helens. An Airborne Topographic Mapper is also used extensively to monitor glaciers and perform coastal change analysis. The combination is also used by soil scientists while creating a soil survey. The detailed terrain modelling allows soil scientists to see slope changes and landform breaks which indicate patterns in soil spatial relationships.

LIDAR has many applications in the field of archaeology including aiding in the planning of field campaigns, mapping features beneath forest canopy, and providing an overview of broad, continuous features that may be indistinguishable on the ground. LIDAR can also provide archaeologists with the ability to create high-resolution digital elevation models (DEMs) of archaeological sites that can reveal micro-topography that are otherwise hidden by vegetation. LIDAR derived products can be easily integrated into a Geographic Information System (GIS) for analysis and interpretation. For example Features that may not be distinguished on the ground or through aerial photography can be identified with the use of visualisation techniques, including analytical hill-shading, slope severity, sky-view factor and insolation modelling, can also be applied where necessary to enhance interpretation of archaeological features. With LIDAR the ability to produce high-resolution datasets quickly and relatively cheaply can be an advantage. Beyond efficiency, its ability to penetrate forest canopy has led to the discovery of features that were not distinguishable through traditional geo-spatial methods and are difficult to reach through field surveys.

Military applications are very similar to commercial use. Their higher resolution makes them particularly good for collecting enough detail to identify targets, such as tanks. LiDAR is proving to be increasingly useful in the autonomy of robots, allowing the computers that control them to produce digital maps of the surrounding area and obsticles. LiDARs ability to differentiate between distances, combined with complex algorithms enable to robots to identify terrain from objects in their path. This kind of autonomy is useful in both UAVs (Unmanned Aerial Vehicles) and specific task autonomous robotics such as bomb disposal units